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Statistics 101: Principles of Statistics11 chapters | 141 lessons | 9 flashcard sets

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Lesson Transcript

Instructor:
*Cathryn Jackson*

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

Using data to describe information can be tricky. The first step is knowing the difference between populations and samples, and then parameters and statistics.

Lenae is campaigning for town mayor. Today, she is doing a little market research to understand the concerns of the people in her town. In order to collect this information, Lenae will have to understand parameters and statistics when working with populations.

In this lesson, you will learn about the differences between parameters and statistics when working with data. But first, let's review populations and samples.

A **population** is all members of a specified group. For example, in Lenae's case she is collecting data on a very literal population, the population of her town. All members of this town would be included in the population. However, most of the time it isn't practical to get information from every member of a population.

When this happens, we have to find a different way of getting information that represents the population without actually asking the whole population. Lenae will probably not have the time and resources to collect information from the entire town; therefore, she will need another approach to getting the information she needs. She will need a sample to gather this information.

A **sample** is a part of a population used to describe the whole group. For example, Lenae could go to a local event or mail out surveys to the people in her town. As long as the information she collects is from the town and not a neighboring community, then this can count as Lenae's sample group.

Samples are used to help the data researcher, in this case Lenae, to understand the entire population. There are many different ways you can get a sample from your population. These include:

- Random sampling
- Simple random sampling
- Cluster sampling
- Stratified sampling
- Systematic sampling

You'll learn more about each of these types of sampling in future lessons!

Now that you understand population and samples, let's discuss how they relate to parameters and statistics.

Lenae has collected the information she needs from her sample. Now it is time to analyze this information using the concepts of parameters and statistics.

A **parameter** is the characteristics used to describe a population. For example, Lenae knows one parameter of her population is that they all live in the same town. This is a 100% known parameter of the population. If Lenae was able to see a 100% accurate census in the town, she could probably find parameters such as the number of people in a certain age range in the population. It's hard to have 100% proof for particular characteristics of a population. This is when we use statistics.

A **statistic** is the characteristics of a sample used to infer information about the population. For example, Lenae is using a sample to analyze data about her town's population. Lenae has found that 64% of the people she surveyed are concerned about the safety of the town's parks. Lenae can use this statistic to infer that approximately 64% of the town is also concerned about the safety of the town's parks.

Now that you understand parameters and statistics, let's practice by identifying the two.

Lenae wants to understand the options of the students at a local high school. She surveys 100 students from the high school asking about how they feel about the safety of the parks. All of the students in the high school are teenagers, and 38% of the students surveyed felt the parks need more lighting to improve safety. Can you find the parameter and statistic from this scenario?

How did you do? In this scenario, the parameters of the population are that all of the high school students are teenagers. From this, you probably already know that the population is the high school students and the sample is the students that were surveyed. Therefore, our statistic is the 38% that describes that students feel that lighting would improve the safety of the parks. Lenae can infer that this 38% can also describe approximately 38% of the population.

When collecting data, you need to understand how to analyze and infer information from the data that you've collected. First, you need to understand the differences between population and samples.

A **population** is all members of a specified group, such as the members of a town or a high school like in Lenae's research. A **sample** is a part of a population used to describe the whole group. This would be the members of the town or high school that Lenae surveyed when collecting information.

Next, you can use parameters and statistics to describe populations and samples. A **parameter** is the characteristics used to describe a population, such as knowing that all of Lenae's population are the members of a town or all high school students are teenagers.

Meanwhile, a **statistic** is the characteristics of a sample used to infer information about the population, such as the 64% of the people surveyed that felt as if the parks were unsafe or the 38% of the high school students surveyed that felt like the parks needed lighting.

You can use all of these concepts to put together and infer information that will help you analyze the data you collect!

After reviewing this lesson, you should have the ability to:

- Define population and sample
- Recall ways to get samples from a population
- Explain the concepts of parameters and statistics and how to use them to analyze data

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Statistics 101: Principles of Statistics11 chapters | 141 lessons | 9 flashcard sets

- Descriptive & Inferential Statistics: Definition, Differences & Examples 5:11
- Difference between Populations & Samples in Statistics 3:24
- Defining the Difference between Parameters & Statistics 5:18
- What is Quantitative Data? - Definition & Examples 4:11
- What is Categorical Data? - Definition & Examples 5:25
- Discrete & Continuous Data: Definition & Examples 3:32
- Nominal, Ordinal, Interval & Ratio Measurements: Definition & Examples 8:29
- The Purpose of Statistical Models 10:20
- Experiments vs Observational Studies: Definition, Differences & Examples 6:21
- Random Selection & Random Allocation: Differences, Benefits & Examples 6:13
- Convenience Sampling in Statistics: Definition & Limitations 6:27
- How Randomized Experiments Are Designed 8:21
- Analyzing & Interpreting the Results of Randomized Experiments 4:46
- Confounding & Bias in Statistics: Definition & Examples 3:59
- Confounding Variables in Statistics: Definition & Examples 5:20
- Bias in Statistics: Definition & Examples 7:24
- Bias in Polls & Surveys: Definition, Common Sources & Examples 4:36
- Misleading Uses of Statistics 8:14
- Go to Overview of Statistics

- Go to Probability

- Go to Sampling

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